539:) we can co-locate records with the same primary and foreign keys on the same node in a cluster. This makes it relatively cheap to join very large tables. No data needs to travel across the network to perform the join. This is very different on Hadoop and HDFS. On HDFS tables are split into big chunks and distributed across the nodes on our cluster. We don’t have any control on how individual records and their keys are spread across the cluster. As a result joins on Hadoop for two very large tables are quite expensive as data has to travel across the network. We should avoid joins where possible. For a large fact and dimension table we can de-normalize the dimension table directly into the fact table. For two very large transaction tables we can nest the records of the child table inside the parent table and flatten out the data at run time.
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clarify what the grain means, you should pick the central process and describe it with one sentence. Furthermore, the grain (sentence) is what you are going to build your dimensions and fact table from. You might find it necessary to go back to this step to alter the grain due to new information gained on what your model is supposed to be able to deliver.
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After describing the business process, the next step in the design is to declare the grain of the model. The grain of the model is the exact description of what the dimensional model should be focusing on. This could for instance be “An individual line item on a customer slip from a retail store”. To
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Extensibility. Dimensional models are scalable and easily accommodate unexpected new data. Existing tables can be changed in place either by simply adding new data rows into the table or executing SQL alter table commands. No queries or applications that sit on top of the data warehouse need to be
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The third step in the design process is to define the dimensions of the model. The dimensions must be defined within the grain from the second step of the 4-step process. Dimensions are the foundation of the fact table, and is where the data for the fact table is collected. Typically dimensions are
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After defining the dimensions, the next step in the process is to make keys for the fact table. This step is to identify the numeric facts that will populate each fact table row. This step is closely related to the business users of the system, since this is where they get access to data stored in
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Understandability. Compared to the normalized model, the dimensional model is easier to understand and more intuitive. In dimensional models, information is grouped into coherent business categories or dimensions, making it easier to read and interpret. Simplicity also allows software to navigate
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Dimensional modeling always uses the concepts of facts (measures), and dimensions (context). Facts are typically (but not always) numeric values that can be aggregated, and dimensions are groups of hierarchies and descriptors that define the facts. For example, sales amount is a fact; timestamp,
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Query performance. Dimensional models are more denormalized and optimized for data querying, while normalized models seek to eliminate data redundancies and are optimized for transaction loading and updating. The predictable framework of a dimensional model allows the database to make strong
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Dimensional modeling does not necessarily involve a relational database. The same modeling approach, at the logical level, can be used for any physical form, such as multidimensional database or even flat files. It is oriented around understandability and performance.
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suffer from performance problems when aggregating or retrieving many dimensional values that may require analysis. If you are only going to do operational reports then you may be able to get by with 3NF because your operational user will be looking for very fine grain
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should cover. Therefore, the first step in the model is to describe the business process which the model builds on. This could for instance be a sales situation in a retail store. To describe the business process, one can choose to do this in plain text or use basic
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There are some arguments on why normalization can be useful. It can be an advantage when part of hierarchy is common to more than one dimension. For example, a geographic dimension may be reusable because both the customer and supplier dimensions use it.
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reprogrammed to accommodate changes. Old queries and applications continue to run without yielding different results. But in normalized models each modification should be considered carefully, because of the complex dependencies between database tables.
532:. Second, we can have a compaction service running in the background that recreates the latest state. Third, we can store our dimension tables in mutable storage, e.g. HBase and federate queries across the two types of storage.
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assumptions about the data which may have a positive impact on performance. Each dimension is an equivalent entry point into the fact table, and this symmetrical structure allows effective handling of complex queries.
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Dimensional normalization or snowflaking removes redundant attributes, which are known in the normal flatten de-normalized dimensions. Dimensions are strictly joined together in sub dimensions.
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databases efficiently. In normalized models, data is divided into many discrete entities and even a simple business process might result in dozens of tables joined together in a complex way.
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Snowflaking has an influence on the data structure that differs from many philosophies of data warehouses. Single data (fact) table surrounded by multiple descriptive (dimension) tables
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product, register#, store#, etc. are elements of dimensions. Dimensional models are built by business process area, e.g. store sales, inventory, claims, etc. Because the different
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nouns like date, store, inventory etc. These dimensions are where all the data is stored. For example, the date dimension could contain data such as year, month and weekday.
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on Hadoop become the default behavior. In order to get the latest and most up to date record in a dimension table we have three options. First, we can create a
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The process of dimensional modeling builds on a 4-step design method that helps to ensure the usability of the dimensional model and the use of the
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frameworks. However, some features of Hadoop require us to slightly adapt the standard approach to dimensional modelling.
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The way data is distributed across HDFS makes it expensive to join data. In a distributed relational database (
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within a business and modelling and implementing these first before adding additional business processes, as a
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The Data
Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing, and Deploying Data Warehouses
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153:, with dimensions surrounding the fact table. To build the schema, the following design model is used:
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share some but not all dimensions, efficiency in design, operation, and consistency, is achieved using
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655:"From Enterprise Models to Dimensional Models: A Methodology for Data Warehouse and Data Mart Design"
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520:. We can only add but not update data. As a result we can only append records to dimension tables.
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advocates a top down design of the model of all the enterprise data using tools such as
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Ralph
Kimball; Margy Ross; Warren Thornthwaite; Joy Mundy; Bob Becker (January 2008).
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Database
Systems - A Practical Approach to Design, Implementation and Management
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Ralph
Kimball; Margy Ross; Warren Thornthwaite; Joy Mundy (10 January 2008).
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181:. The basics in the design build on the actual business process which the
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The Data
Warehouse Toolkit: The Definitive Guide to Dimensional Modeling
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The Data
Warehouse Toolkit: The Complete Guide to Dimensional Modeling
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Developers often don't normalize dimensions due to several reasons:
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which includes a set of methods, techniques and concepts for use in
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for star-joined databases is simple, predictable, and controllable.
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Performance can be slower, due to the many joins between tables
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Data
Warehouse Design: Modern Principles and Methodologies
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632:(6th ed.). Pearson. Part 9 Business Intelligence.
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Connolly, Thomas; Begg, Carolyn (26 September 2014).
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Benefits of the dimensional model are the following:
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design. The approach focuses on identifying the key
500:We still get the benefits of dimensional models on
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Normalization makes the data structure more complex
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716:Matteo Golfarelli; Stefano Rizzi (26 May 2009).
363:, potentially preventing the article from being
1282:Data warehousing products and their producers
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46:Please help adding a more precise page range.
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741:Ralph Kimball; Margy Ross (26 April 2002).
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468:. Please do not remove this message until
383:reliable, independent, third-party sources
268:. Please do not remove this message until
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488:Learn how and when to remove this message
401:Learn how and when to remove this message
288:Learn how and when to remove this message
62:Learn how and when to remove this message
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464:Relevant discussion may be found on the
435:Dimensional models, Hadoop, and big data
377:by replacing them with more appropriate
264:Relevant discussion may be found on the
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528:that retrieves the latest record using
360:too closely associated with the subject
190:(BPMN) or other design guides like the
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653:Moody, Daniel L.; Kortink, Mark A.R.
768:The Data Warehouse Lifecycle Toolkit
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145:The dimensional model is built on a
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592:Margy Ross (Kimball Group) (2005).
188:Business Process Model and Notation
32:This article cites its sources but
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1167:MultiDimensional eXpressions (MDX)
577:"A Dimensional Modeling Manifesto"
14:
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667:from the original on 17 May 2017
594:"Identifying Business Processes"
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358:may rely excessively on sources
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339:Benefits of dimensional modeling
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103:. An alternative approach from
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1188:Business intelligence software
1067:Extract, load, transform (ELT)
1062:Extract, transform, load (ETL)
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84:Business Dimensional Lifecycle
1:
1136:Decision support system (DSS)
720:. McGraw-Hill Osborne Media.
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543:
316:The space savings are minimal
1162:Data Mining Extensions (DMX)
109:entity-relationship modeling
7:
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923:Ensemble modeling patterns
893:Single version of the truth
470:conditions to do so are met
270:conditions to do so are met
171:Choose the business process
157:Choose the business process
10:
1332:
1277:Comparison of OLAP servers
770:(Second ed.). Wiley.
745:(Second ed.). Wiley.
690:(Second ed.). Wiley.
598:Kimball Group, Design Tips
522:Slowly Changing Dimensions
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1146:Data warehouse automation
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831:Creating a data warehouse
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660:. Dimensional Modelling.
581:DBMS and Internet Systems
192:Unified Modeling Language
87:methodology developed by
1172:XML for Analysis (XMLA)
557:(3rd ed.). Wiley.
235:Dimension normalization
209:Identify the dimensions
163:Identify the dimensions
1104:Using a data warehouse
959:Operational data store
122:business process areas
1121:Business intelligence
553:; Margy Ross (2013).
16:Data modeling concept
937:Focal point modeling
909:Column-oriented DBMS
858:Dimensional modeling
600:(69). Archived from
126:conformed dimensions
75:Dimensional modeling
38:ranges are too broad
1242:Information factory
1015:Early-arriving fact
932:Data vault modeling
883:Reverse star schema
530:windowing functions
457:of this section is
325:Query performance.
257:of this section is
141:Designing the model
1193:Reporting software
514:Hadoop File System
424:Query optimization
220:Identify the facts
101:bottom-up approach
97:business processes
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989:Sixth normal form
777:978-0-470-14977-5
727:978-0-07-161039-1
697:978-0-470-14977-5
639:978-1-292-06118-4
564:978-1-118-53080-1
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198:Declare the grain
166:Identify the fact
160:Declare the grain
81:) is part of the
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604:on 12 June 2013.
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669:. Retrieved
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602:the original
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504:and similar
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1198:Spreadsheet
1131:Data mining
873:Star schema
115:Description
1305:Categories
1237:Bill Inmon
1041:Degenerate
1010:Fact table
610:References
544:Literature
455:neutrality
375:improve it
365:verifiable
255:neutrality
1155:Languages
1141:OLAP cube
1126:Dashboard
1077:Transform
1029:Dimension
984:Data mart
919:Data mesh
888:Aggregate
853:Dimension
518:immutable
478:June 2018
466:talk page
391:June 2018
379:citations
278:June 2018
266:talk page
52:June 2018
42:incorrect
1270:Products
1114:Concepts
979:Metadata
968:Elements
914:Data hub
902:Variants
848:Database
841:Concepts
662:Archived
575:(1997).
506:big data
459:disputed
259:disputed
1220:Related
1072:Extract
1055:Filling
1020:Measure
369:neutral
194:|UML).
1230:People
774:
749:
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671:3 July
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502:Hadoop
111:(ER).
1181:Tools
954:ROLAP
949:MOLAP
944:HOLAP
665:(PDF)
658:(PDF)
330:data.
105:Inmon
1082:Load
1003:Fact
868:OLAP
863:Fact
772:ISBN
747:ISBN
722:ISBN
692:ISBN
673:2018
634:ISBN
587:(9).
559:ISBN
526:View
512:The
452:The
367:and
252:The
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